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{{Paper
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|storemode=property
|title=Developing a Patient Safety Ontology for Knowledge Management, Data Integration, and Decision Making
|pdfUrl=https://ceur-ws.org/Vol-1327/icbo2014_paper_48.pdf
|volume=Vol-1327
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==Developing a Patient Safety Ontology for Knowledge Management, Data Integration, and Decision Making==
https://ceur-ws.org/Vol-1327/icbo2014_paper_48.pdf
ICBO 2014 Proceedings
Developing a patient safety ontology for
knowledge management, data integration,
and decision making
Chen Liang1, Yang Gong2
University of Texas Health Science Center
Houston, Texas, United States
1
Chen.Liang@uth.tmc.edu
2
Yang.Gong@uth.tmc.edu
There has been a pressing need for improving patient safety. Sizable amount of Americans do not feel safe
about health care, as it is supposed to be [1]. Meanwhile, preventable medical errors that harm patients cost
$17.1 billion a year which over-burdened the healthcare system [2]. Although the reasons why errors
happen can be complex due to the intricate specification of the system, much attention has been drawn to
how patient safety event reporting system can improve the quality and safety of health service over the past
decade [3]. An outstanding event reporting system should be able to collect data that link to procedures and
factors threaten patient safety in a timely manner. Nevertheless, a great number of reporting systems are
suffering low quality of the data, inefficiency and ineffectiveness of data entry [3-5]. One approach aiming
at improving the situation is developing a comprehensive and unified ontology for patient safety events.
Over the last decade, there has seen a dramatic increase of 600% in the number of citations on ontologies in
PubMed/MEDLINE [6], however, the ontology engineering is relatively lagged behind in the field of
patient safety. Therefore, the development of ontology towards enhancing patient safety is in an imperative
need.
Ontologies or taxonomies developed specifically for use in patient safety system are not new. The
Australian Patient Safety Foundation (APSF) originally reported the Australian Incident Monitoring
System in 1987, and later in 1993 and 2000, APSF expanded the system twice [7]. A newly developed
taxonomy endeavors to categorize major types of human error contributing to medical errors [8]. Other
taxonomies or standards such as JACHO patient safety event taxonomy [9], National coordinating council
for medication error reporting and prevention (NCC MERP)’s taxonomy of medication errors [10],
Neonatal Intensive Care system (NIC) [11], Pediatric Patient Safety taxonomy (PED) [12], Preliminary
Taxonomy of medical errors in Family Practice (PTFP) [13], Taxonomy of Nursing Errors (TNE) [14], and
Adverse Event Reporting Ontology (AERO) [15] shared insights in specific domains. While these
ontologies served primarily as standards of domain specific taxonomies, the rapid increase in medical
information calls for a unified knowledgebase with unified language system to be used as a common
denominator for sharing and learning across patient safety reporting systems.
In this project, we built a semantic web ontology (Medeon) using W3C open standard Web Ontology
Language (OWL) (Fig. 1). The Common Formats (v1.2) developed by the Agency for Healthcare and
Research Quality (AHRQ) were employed as the taxonomy where we extracted and encoded semantic
knowledge into Medeon. Recognized as a unified standard of reporting patient safety events, the Common
Formats are designed to specify and collect event information, which range from general concerns to
frequently occurring and serious types of the events. We chose OWL and semantic web technologies
because they jointly provide unique advantages for machine understandable semantics and descriptive logic
reasoning which allows us to model real-world patient safety data in a computerized system. We borrowed
the hierarchical structure in the Common Formats to build the OWL classes and rephrased the narrative
data in the Common Formats to construct OWL incidents and objective properties. In order to share the
knowledge of the Common Formats with other domain ontologies, Unified Medical Language System
(UMLS) was employed to map terminologies between different domains.
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ICBO 2014 Proceedings
!!
Fig. 1. A general procedure of generating Medeon. Images were adapted from AHRQ Common Formats
(https://www.pso.ahrq.gov/common) and US National Library of Medicine. (http://www.nlm.nih.gov/research/umls/)
Our ontology lay out to improve patient safety reporting system in the following aspects. The ontology
primarily serves as a knowledgebase to model the taxonomies broadly used for patient safety events. With
this knowledgebase, semantic data can be retrieved and reasoned through descriptive logic rules and
applied to text mining methods. Secondly, the use of UMLS provides a framework to encode and exchange
data between our ontology and other semantic data repositories. In the end, our ontology holds promise in
facilitating decision support in clinical research [16]. However, it is most challenging in mapping between
discrepant data sources due to the distinction among existing taxonomies in terms of the hierarchical
structure and terminologies. The next step will focus on this issue by evaluating the current ontology with
event reports.
This project is supported by a grant on patient safety from the University of Texas System.
Reference
[1] Kohn, L.T., J.M. Corrigan, and M.S. Donaldson, To Err Is Human:: Building a Safer Health System. Vol. 627. 2000: National
Academies Press.
[2] Van Den Bos, J., et al., The $17.1 billion problem: the annual cost of measurable medical errors. Health Affairs, 2011. 30(4): p.
596-603.
[3] Pronovost, P.J., et al., Improving the value of patient safety reporting systems. Advances in patient safety: new directions and
alternative approaches, 2008. 1.
[4] Gong, Y. Terminology in a voluntary medical incident reporting system: a human-centered perspective. in Proceedings of the 1st
ACM International Health Informatics Symposium. 2010. ACM.
[5] Gong, Y., Data consistency in a voluntary medical incident reporting system. J Med Syst, 2011. 35(4): p. 609-15.
[6] Bodenreider, O., Biomedical ontologies in action: role in knowledge management, data integration and decision support.
Yearbook of medical informatics, 2008: p. 67.
[7] Spigelman, A.D. and J. Swan, Review of the Australian incident monitoring system. ANZ journal of surgery, 2005. 75(8): p. 657-
661.
[8] Zhang, J., et al., A cognitive taxonomy of medical errors. Journal of biomedical informatics, 2004. 37(3): p. 193-204.
[9] Chang, A., et al., The JCAHO patient safety event taxonomy: a standardized terminology and classification schema for near
misses and adverse events. International Journal for Quality in Health Care, 2005. 17(2): p. 95-105.
[10] Brixey, J., T.R. Johnson, and J. Zhang. Evaluating a medical error taxonomy. in Proceedings of the AMIA Symposium. 2002.
American Medical Informatics Association.
[11] Suresh, G., et al., Voluntary anonymous reporting of medical errors for neonatal intensive care. Pediatrics, 2004. 113(6): p.
1609-1618.
[12] Woods, D.M., et al., Anatomy of a patient safety event: a pediatric patient safety taxonomy. Quality and Safety in Health Care,
2005. 14(6): p. 422-427.
[13] Dovey, S.M., et al., A preliminary taxonomy of medical errors in family practice. Quality and Safety in Health Care, 2002.
11(3): p. 233-238.
[14] Woods, A. and S. Doan-Johnson, Toward a Taxonomy of Nursing Practice Errors. Nursing Management, 2002. 33(10): p. 45-
48.
[15] Courtot, M., R.R. Brinkman, and A. Ruttenberg, The Logic of Surveillance Guidelines: An Analysis of Vaccine Adverse Event
Reports from an Ontological Perspective. PloS one, 2014. 9(3): p. e92632.
[16] Huff, S.M. and R.A. Greenes, Ontologies, vocabularies, and data models. Clinical Decision Support: The Road Ahead, 2007: p.
307-421.
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ICBO 2014 Proceedings
Developing a patient safety ontology for knowledge
management, data integration, and decision making
Chen Liang, MS, Yang Gong, MD, PhD
The School of Biomedical Informatics |The University of Texas Health Science Center at Houston
Introduction General Framework
• Although the importance of patient safety has been increasingly recognized across the
world, the reduction and prevention of safety events are not as good as expected
• Recently, much attention has been paid to patient safety reporting system which holds
promise in improving the situation.
• One roadblock in the system has been the lack of comprehensive taxonomy on patient
safety events to support knowledge management, data integration, and decision making.
• To meet this imperative need, we aim at developing a unified semantic web ontology of
Fig 1.General framework of constructing Medeon.
patient safety events to serve as a general guideline.
Mapping Procedure Conclusion and Future Steps
A ! Step1, we retained the original hierarchical structure in the Common Our ontology layout to improve patient safety
Formats and formed a meta ontology which contains four OWL classes and reporting system in the following aspects.
has a maximal depth of four. Fig 2A is an example of the event description
form which summarizes the overall hierarchical structure of the patient • It serves as a knowledgebase to model the
safety events in the Common Formats. Fig 2B is an example of Healthcare taxonomies broadly used for patient safety
Event Reporting Form (HERF) events.
• With this knowledgebase, semantic data
! Step 2we manually rephrased the entities before adding can grow as the knowledge to keep with the
B them as OWL classes to the ontologyPart of the entities in development in the real-world
the Common Formats were judged as OWL instances
therefore were imported to the ontology as OWL instances. • Semantic data can be retrieved and
See Fig 3. reasoned by using descriptive logic rules
and applied to text mining methods.
• It holds promise to largely facilitate decision
support in clinical research.
Fig 2. An example of the mapping procedure Our next step will address,
! Step 3, we are collaborating with domain experts to • To add knowledge from other existing
define and evaluate the OWL object properties since patient safety taxonomies into Medeon,
the Common Formats do not provide semantic data in • To use named entity recognizer and UMLS
guiding OWL object properties. to label from real-world data and further
improve Medeon,
! Step 4, after the semantic representation was
• To perform evaluation using real patient
established, we were able to use the ontology to
safety reports.
perform tasks such as consistency checking,
automatic classification and semantic reasoning This project is in part supported by a grant on patient safety
from the University of Texas System. PI: Yang Gong. Contact
Fig is a screenshot from OntoGrafa build-in visualization tool in Protégé 4.3.0. The screenshot
depicts the top two levels of OWL classes mapped from the Common Formats. chen.liang@uth.tmc.edu for any comments or suggestions.
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